If you’re weighing the benefits of adding machine learning technology to your company’s marketing toolkit, it can be difficult to know where to start. Surging interest in machine learning has resulted in widespread hype and the application of strategies that aren’t actually intelligent or responsive. Still, machine learning is the future of decision making and a crucial AI technology, and businesses that operate in dynamic, challenging markets can use it to cultivate a lasting competitive edge. Ahead, learn what you need to consider to achieve your marketing goals with machine learning.

What Problems Can Machine Learning Solve?

Machine learning models are best reserved for use cases where the information that guides decision making is subject to frequent change. For instance, if it’s always true that customers should be connected with a representative when they say a certain phrase in a phone tree, there’s no need to add complexity with machine learning. However, it can improve outcomes in areas like credit card fraud, where malicious behavior is always being modified in response to heightened security measures, so it isn’t possible to establish concrete axioms about what constitutes fraud. Machine learning can change the behaviors it flags as fraud as the behaviors themselves change.

To take advantage of this capability, it’s helpful if your business has access to current, well-organized data that’s related to the problem you need to solve. As humans do, machines learn by gathering data, although different types of machine learning architecture take unique approaches. Consider the example of fraudulent purchases; if you need to train a model to detect unauthorized credit card use, you can start by providing several examples of transactions you already know to be fraudulent. This is considered “supervised” learning, where the model looks for a certain pattern—in this case, discrepancies between customer behavior and suspicious charges that typically indicate fraud—that its human users already know to exist.

This differs from “unsupervised” learning, in which the model uses unsorted data to look for new patterns. An unsupervised model tasked with detecting credit card fraud might try to detect new associations between suspicious charges and customer behavior.

How Should You Provide Models With Data?

Your machine learning model can’t learn or draw conclusions without ingesting data. Manual data input and processing can not only introduce error but is also time- and resource-intensive. You can use a data connector to combine and warehouse multiple data inputs to make sure data acquisition is as streamlined as possible.

Data can be ingested via batch processing or real-time streaming; the first provides data on a schedule, while the second continually dispatches new information to the data warehouse as soon as it’s received. Extremely small, frequently updated batches can mimic streaming updates, but it’s not exactly the same. Real-time streaming is considered preferable for AI applications, but it is typically more expensive than batching.

How Will You Receive Your Model's Output?

The reason machine learning is exciting is the output it produces—so how will you access it? In practice, most businesses will need to be able to send AI output to several platforms with various operating parameters, so a stable API solution is necessary. API stands for Application Programming Interface; whenever you use an app on your phone to check the stock market or the weather, you’re using an API that fetches information and displays it in an intelligible way.

Representational State Transfer, or REST, APIs are considered best practice for working with several disparate requesting platforms because, among other reasons, they standardize requests and don’t require the server that stores data to remember any information about that request, so whenever a client queries the API, the request itself has all the information the server needs to fulfill it.

Machine learning solutions can transform your business’s approach to understanding and using data, but for most organizations, making the system work with existing technology and ensuring its results are accessible are major obstacles. Planning your machine learning integration around effective data pipeline structures and a REST API will allow you to flexibly assimilate it into your current data practices.